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Notes from papers I'm reading, mostly NLP

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Paper notes

Notes from papers I'm reading, ordered by topic and chronologically.

NLP

  1. Conditional Random Fields: probabilistic models for segmenting and labeling sequence data, Lafferty et al, 2001 [Paper] [Notes] #nlp #architectures
  2. Introduction to the CoNLL-2003 shared task: language-independent named entity recognition, Sang et al., 2003 [Paper] [Notes] #nlp #datasets
  3. Bidirectional LSTM-CRF Models for sequence tagging, Huang et al., 2015 [Paper] [Notes] #nlp #architectures
  4. Neural Machine Translation of Rare Words with Subword Units, Sennrich et al., 2015 [Paper] [Notes] #nlp
  5. Neural Architectures for Named Entity Recognition, Lample et al., 2016 [Paper] [Notes] #nlp #architectures #NER
  6. Named Entity Recognition with Bidirectional LSTM-CNNs, Chiu et al., 2016 [Paper] [Notes] #nlp #architectures
  7. Semi-supervised sequence tagging with bidirectional language models, Peters et al., 2017 [Paper] [Notes] #nlp #embeddings
  8. Attention is all you need, Vaswani et al., 2018 [Paper] [Notes] #nlp #architectures
  9. Deep contextualized word representations, Peters et al., 2018 [Paper] [Notes] #nlp #embeddings
  10. Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable, Hangya et al., 2018 [Paper] [Notes] #nlp
  11. A Named Entity Recognition Shootout for German, Riedl and Padó, 2018 [Paper] [Notes] #nlp #NER #datasets
  12. SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference, Zellers et al., 2018 [Paper] [Notes] #nlp #datasets
  13. Dissecting contextual word embeddings: architecture and representation, Peters et al., 2018 [Paper] [Notes] #nlp #embeddings
  14. Contextual string embeddings for sequence labeling, Akbik et al., 2018 [Paper] [Notes] #nlp #embeddings
  15. Targeted synctactic evaluation of language models, Marvin and Linzen, 2018 [Paper] [Notes] #nlp #linguistics
  16. BERT: Pre-training of deep bidirectional transformers for language understanding, Devlin et al., 2018 [Paper] [Notes] #nlp #embeddings
  17. Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia, Yamada et al., 2018 [Paper] [Notes] #nlp #embeddings
  18. Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference., McCoy et al., 2019 [Paper] [Notes] #nlp #linguistics #datasets
  19. Linguistic Knowledge and Transferability of Contextual Representations, Liu et al., 2019 [Paper] [Notes] #nlp
  20. What do you learn from context? Probing for sentence structure in contextualized word representations, Tenney et al., 2019 [Paper] [Notes] #nlp
  21. HellaSwag: Can a Machine Really Finish Your Sentence?, Zellers et al., 2019 [Paper] [Notes] #nlp #datasets
  22. Flair: an easy-to-use framework for stat-of-the-art NLP [Paper] [Notes] #nlp #embeddings
  23. Towards Robust Named Entity Recognition for Historic German, Schweter et al., 2019 [Paper] [Notes] #nlp #NER
  24. XLNet: generalized autoregressive pretraining for language understanding, Yang et al., 2019 [Paper] [Notes] #nlp #architectures
  25. R-Transformer: Recurrent Neural Network Enhanced Transformer, Wang et al., 2019 [Paper] [Notes] #nlp #architectures
  26. Probing Neural Network Comprehension of Natural Language Arguments, Nivel and Kao, 2019 [Paper] [Notes] #nlp #datasets
  27. Language Models as Knowledge Bases?, Petroni et al., 2019 [Paper] [Notes] #nlp #linguistics
  28. HuggingFace's Transformers: State-of-the-art Natural Language Processing, Wolf et al., 2019 [Paper] [Notes] #nlp #frameworks
  29. Evaluating the Factual Consistency of Abstractive Text Summarization, Kryscinski et al., 2019 [Paper] [Notes] #nlp #text-summarization
  30. Generalization through Memorization: Nearest Neighbor Language Models, Khandelwal et al., 2019 [Paper] [Notes] #nlp #architectures
  31. BERT is Not a Knowledge Base (Yet): Factual Knowledge vs. Name-Based Reasoning in Unsupervised QA, Poerner et al., 2019 [Paper] [Notes] #nlp #embeddings
  32. Single Headed Attention RNN: Stop Thinking With Your Head, Merity, 2019 [Paper] [Notes] #nlp #architectures
  33. What’s Going On in Neural Constituency Parsers? An Analysis, Gaddy et al., 2018 [Paper] [Notes] #nlp
  34. BPE-Dropout: simple and effective subword regularization, Provilkov et al., 2019 [Paper] [Notes] #nlp
  35. Pre-trained Models for Natural Language Processing: A Survey, Qiu et al., 2020 [Paper] #nlp

Embeddings

  1. Semi-supervised sequence tagging with bidirectional language models, Peters et al., 2017 [Paper] [Notes] #nlp #embeddings
  2. Deep contextualized word representations, Peters et al., 2018 [Paper] [Notes] #nlp #embeddings
  3. Dissecting contextual word embeddings: architecture and representation, Peters et al., 2018 [Paper] [Notes] #nlp #embeddings
  4. BERT: Pre-training of deep bidirectional transformers for language understanding, Devlin et al., 2018 [Paper] [Notes] #nlp #embeddings
  5. Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia, Yamada et al., 2018 [Paper] [Notes] #nlp #embeddings
  6. BERT is Not a Knowledge Base (Yet): Factual Knowledge vs. Name-Based Reasoning in Unsupervised QA, Poerner et al., 2019 [Paper] [Notes] #nlp #embeddings

Architectures

  1. Conditional Random Fields: probabilistic models for segmenting and labeling sequence data, Lafferty et al, 2001 [Paper] [Notes] #nlp #architectures
  2. Bidirectional LSTM-CRF Models for sequence tagging, Huang et al., 2015 [Paper] [Notes] #nlp #architectures
  3. Neural Architectures for Named Entity Recognition, Lample et al., 2016 [Paper] [Notes] #nlp #architectures #NER
  4. Named Entity Recognition with Bidirectional LSTM-CNNs, Chiu et al., 2016 [Paper] [Notes] #nlp #architectures
  5. Attention is all you need, Vaswani et al., 2018 [Paper] [Notes] #nlp #architectures
  6. Reasoning with Sarcasm by Reading In-between, Tay et al., 2018 [Paper] [Notes] #sarcasm-detection #architectures
  7. XLNet: generalized autoregressive pretraining for language understanding, Yang et al., 2019 [Paper] [Notes] #nlp #architectures
  8. R-Transformer: Recurrent Neural Network Enhanced Transformer, Wang et al., 2019 [Paper] [Notes] #nlp #architectures
  9. Generalization through Memorization: Nearest Neighbor Language Models, Khandelwal et al., 2019 [Paper] [Notes] #nlp #architectures
  10. Single Headed Attention RNN: Stop Thinking With Your Head, Merity, 2019 [Paper] [Notes] #nlp #architectures
  11. A Transformer-based approach to Irony and Sarcasm detection, Potamias et al., 2019 [Paper] [Notes] #sarcasm-detection #architecture

Frameworks

  1. Flair: an easy-to-use framework for stat-of-the-art NLP [Paper] [Notes] #nlp #frameworks
  2. HuggingFace's Transformers: State-of-the-art Natural Language Processing, Wolf et al., 2019 [Paper] [Notes] #nlp #frameworks
  3. Selective Brain Damage: Measuring the Disparate Impact of Model Pruning, Hooker et al., 2019 [Paper] [Notes] #frameworks

Datasets

  1. Introduction to the CoNLL-2003 shared task: language-independent named entity recognition, Sang et al., 2003 [Paper] [Notes] #nlp #datasets
  2. SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference, Zellers et al., 2018 [Paper] [Notes] #nlp #datasets
  3. A Named Entity Recognition Shootout for German, Riedl and Padó, 2018 [Paper] [Notes] #nlp #NER #datasets
  4. Probing Neural Network Comprehension of Natural Language Arguments, Nivel and Kao, 2019 [Paper] [Notes] #nlp #datasets
  5. Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference., McCoy et al., 2019 [Paper] [Notes] #nlp #linguistics #datasets
  6. UR-FUNNY: A Multimodal Language Dataset for Understanding Humor, Hasan et al., 2019 [Paper] [Notes] #sarcasm-detection #datasets
  7. HellaSwag: Can a Machine Really Finish Your Sentence?, Zellers et al., 2019 [Paper] [Notes] #nlp #datasets
  8. Sentiment analysis is not solved! Assessing and probing sentiment classification, Barnes et al., 2019 [Paper] [Notes] #nlp #datasets
  9. Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model, Cai et al., 2019 [Paper] [Notes] #sarcasm-detection #datasets
  10. Towards Multimodal Sarcasm Detection (An Obviously Perfect Paper), Castro et al., 2019 [Paper] [Notes] #sarcasm-detection #datasets
  11. iSarcasm: A Dataset of Intended Sarcasm, Oprea et al., 2019 [Paper] [Notes] #datasets #sarcasm-detection

NER

  1. Introduction to the CoNLL-2003 shared task: language-independent named entity recognition, Sang et al., 2003 [Paper] [Notes] #nlp #datasets #NER
  2. Neural Architectures for Named Entity Recognition, Lample et al., 2016 [Paper] [Notes] #nlp #architectures #NER
  3. Named Entity Recognition with Bidirectional LSTM-CNNs, Chiu et al., 2016 [Paper] [Notes] #nlp #architectures #NER
  4. Towards Robust Named Entity Recognition for Historic German, Schweter et al., 2019 [Paper] [Notes] #nlp #NER
  5. A Named Entity Recognition Shootout for German, Riedl and Padó, 2018 [Paper] [Notes] #nlp #NER #datasets

Sarcasm detection

summary

  1. Sarcasm Detection on Twitter: A Behavioral Modeling Approach, Rajadesingan et al., 2015 [Paper] [Notes] #sarcasm-detection
  2. Contextualized Sarcasm Detection on Twitter, Bamman and Smith, 2015 [Paper] [Notes] #sarcasm-detection
  3. Harnessing Context Incongruity for Sarcasm Detection, Joshi et al., 2015 [Paper] [Notes] #linguistics #sarcasm-detection
  4. Automatic Sarcasm Detection: A Survey, Joshi et al., 2017 [Paper] [Notes] #sarcasm-detection
  5. Detecting Sarcasm is Extremely Easy ;-), Parde and Nielsen, 2018 [Paper] [Notes] #sarcasm-detection
  6. CASCADE: Contextual Sarcasm Detection in Online Discussion Forums, Hazarika et al., 2018 [Paper] [Notes] #sarcasm-detection
  7. Reasoning with Sarcasm by Reading In-between, Tay et al., 2018 [Paper] [Notes] #sarcasm-detection #architectures
  8. Tweet Irony Detection with Densely Connected LSTM and Multi-task Learning, Wu et al., 2018 [Paper] [Notes] #sarcasm-detection
  9. UR-FUNNY: A Multimodal Language Dataset for Understanding Humor, Hasan et al., 2019 [Paper] [Notes] #sarcasm-detection #datasets
  10. Exploring Author Context for Detecting Intended vs Perceived Sarcasm, Oprea and Magdy, 2019 [Paper] [Notes] #sarcasm-detection
  11. Towards Multimodal Sarcasm Detection (An Obviously Perfect Paper), Castro et al., 2019 [Paper] [Notes] #sarcasm-detection #datasets
  12. Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model, Cai et al., 2019 [Paper] [Notes] #sarcasm-detection #datasets
  13. A2Text-Net: A Novel Deep Neural Network for Sarcasm Detection, Liu et al., 2019 [Paper] [Notes] #sarcasm-detection
  14. Sarcasm detection in tweets, Rajagopalan et al., 2019 [Paper] [Notes] #sarcasm-detection
  15. A Transformer-based approach to Irony and Sarcasm detection, Potamias et al., 2019 [Paper] [Notes] #sarcasm-detection #architecture
  16. Deep and dense sarcasm detection, Pelser et al., 2019 [Paper] [Notes] #sarcasm-detection
  17. iSarcasm: A Dataset of Intended Sarcasm, Oprea et al., 2019 [Paper] [Notes] #datasets #sarcasm-detection

Text summarization

  1. Evaluating the Factual Consistency of Abstractive Text Summarization, Kryscinski et al., 2019 [Paper] [Notes] #nlp #text-summarization

Reinforcement learning

  1. Theory of Minds: Understanding Behavior in Groups Through Inverse Planning, Shum et al., 2019 [Paper] [Notes] #reinforcement-learning #social-sciences
  2. The Hanabi Challenge: A New Frontier for AI Research, Bard et al., 2019 [Paper] [Notes] #reinforcement-learning
  3. Mastering Atari, Go, Chess and Shogi by Planning with a learned model, Schrittwieser et al., 2019 [Paper] [Notes] #reinforcement-learning

Computer vision

  1. Cubic Stylization, Derek Liu and Jacobson, 2019 [Paper] [Notes] #computer-vision

Linguistics

  1. Moving beyond the plateau: from lower-intermediate to upper-intermediate, Richards, 2015 [Paper] [Notes] #linguistics
  2. Harnessing Context Incongruity for Sarcasm Detection, Joshi et al., 2015 [Paper] [Notes] #linguistics #sarcasm-detection
  3. A Trainable Spaced Repetition Model for Language Learning, Settles and Meeder, 2016 [Paper] [Notes] #linguistics
  4. Targeted synctactic evaluation of language models, Marvin and Linzen, 2018 [Paper] [Notes] #nlp #linguistics
  5. Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference., McCoy et al., 2019 [Paper] [Notes] #nlp #linguistics #datasets
  6. Language Models as Knowledge Bases?, Petroni et al., 2019 [Paper] [Notes] #nlp #linguistics
  7. Different languages, similar encoding efficiency: Comparable information rates across the human communicative niche, Coupé et al., 2019 [Paper] [Notes] #linguistics #social-sciences
  8. My English sounds better than yours: Second language learners perceive their own accent as better than that of their peers, Mittlerer et al., 2020 [Paper] [Notes] #linguistics

Social sciences

  1. How much does education improve intelligence? A meta-analysis, Ritchie et al., 2017 [Paper] [Notes] #social-sciences
  2. Theory of Minds: Understanding Behavior in Groups Through Inverse Planning, Shum et al., 2019 [Paper] [Notes] #reinforcement-learning #social-sciences
  3. Fake news game confers psychological resistance against online misinformation, Roozenbeek and van der Linden, 2019 [Paper] [Notes] #social-sciences #humanities
  4. Different languages, similar encoding efficiency: Comparable information rates across the human communicative niche, Coupé et al., 2019 [Paper] [Notes] #linguistics #social-sciences
  5. Kids these days: Why the youth of today seem lacking, Protzko and Schooler, 2019 [Paper] [Notes] #social-sciences

Humanities

  1. Fake news game confers psychological resistance against online misinformation, Roozenbeek and van der Linden, 2019 [Paper] [Notes] #social-sciences #humanities

Physics

  1. First-order transition in a model of prestige bias, Skinner, 2019 [Paper] [Notes] #physics

Neuroscience

  1. A deep learning framework for neuroscience, Richard et al., 2019 [Paper] [Notes] #neuroscience

Algorithms

  1. Replace or Retrieve Keywords In Documents At Scale, Singh, 2017 [Paper] [Notes] #algorithms

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Notes from papers I'm reading, mostly NLP